Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions

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Published Jun 27, 2024
Alireza Javanmardi Osarenren Kennedy Aimiyekagbon Amelie Bender James Kuria Kimotho Walter Sextro Eyke Hüllermeier

Abstract

This paper investigates the remaining useful lifetime (RUL) estimation of bearings under dynamic, i.e., time-varying, operating conditions (OC). Unlike conventional studies that assume constant OC in bearing accelerated life tests, we introduce a dataset with time-varying OC during run-to-failure experiments, simulating real-world scenarios. We explore data-driven approaches to identify the transition point from a healthy to an unhealthy state and estimate the RUL. Additionally, we examine strategies for integrating OC information to enhance RUL estimations. These methodologies are evaluated through numerical experiments using various machine learning algorithms. 

How to Cite

Javanmardi, A., Aimiyekagbon, O. K., Bender, A. ., Kimotho, J. K., Sextro, W., & Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. PHM Society European Conference, 8(1), 9. https://doi.org/10.36001/phme.2024.v8i1.4101
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Keywords

Remaining useful lifetime estimation, Bearing datasets, Time-varying operating conditions, Machine learning

References
Aimiyekagbon, O. K. (2024). Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions. Zenodo. Retrieved from https://doi.org/10.5281/zenodo.10805042 (Data set)

Alewine, K., & Chen, W. (2010). Wind turbine generator failure modes analysis and occurrence. In Wind power 2010 conference, Dallas.

da Costa, P. R. d. O., Akçay, A., Zhang, Y., & Kaymak, U. (2020). Remaining useful lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety, 195.

DFG. (2017). Schwingungsanalysesystem für automatisierte Schwingungsmessungen. Major research instrumentation supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the Project number 391178551.

Ding, Y., Ding, P., Zhao, X., Cao, Y., & Jia, M. (2022). Transfer learning for remaining useful life prediction across operating conditions based on multisource domain adaptation. IEEE/ASME Transactions on Mechatronics, 27.

Ding, Y., Jia, M., & Cao, Y. (2021). Remaining useful life estimation under multiple operating conditions via deep subdomain adaptation. IEEE Transactions on Instrumentation and Measurement, 70.

Ding, Y., Jia, M., Miao, Q., & Huang, P. (2021). Remaining useful life estimation using deep metric transfer learning for kernel regression. Reliability Engineering & System Safety, 212.

Du, W., Hou, X., & Wang, H. (2022). Time-varying degradation model for remaining useful life prediction of rolling bearings under variable rotational speed. Applied Sciences, 12. Retrieved from https://www.mdpi.com/2076-3417/12/8/4044

Fan, Y., Nowaczyk, S., & Rögnvaldsson, T. (2020). Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliability Engineering & System Safety, 203.

Fu, S., Zhong, S., Lin, L., & Zhao, M. (2021). A novel time-series memory auto-encoder with sequentially updated reconstructions for remaining useful life prediction. IEEE Transactions on Neural Networks and Learning Systems, 33.

Huang, C.-G., Huang, H.-Z., & Li, Y.-F. (2019). A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Transactions on Industrial Electronics, 66.

Javanmardi, A., & Hüllermeier, E. (2023). Conformal prediction intervals for remaining useful lifetime estimation. International Journal of Prognostics and Health Management, 2.

Lee, J., Qiu, H., Yu, G., Lin, J., & Services, R. T. (2007). IMS, university of cincinnati. Bearing data set. NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA.

Li, F., Zhang, L., Chen, B., Gao, D., Cheng, Y., Zhang, X., ... Huang, Z. (2020). An optimal stacking ensemble for remaining useful life estimation of systems under multi-operating conditions. IEEE Access, 8.

Li, N., Gebraeel, N., Lei, Y., Bian, L., & Si, X. (2019). Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model. Reliability Engineering & System Safety, 186. Retrieved from https://www.sciencedirect.com/science/article/pii/S0951832018313024

Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multiscale feature extraction. Reliability Engineering & System Safety, 182, 208–218.

Mao, W., He, J., & Zuo, M. J. (2019). Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Transactions on Instrumentation and Measurement, 69.

Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). Pronostia: An experimental platform for bearings accelerated degradation tests. In IEEE international conference on prognostics and health management, PHM’12 (pp. 1–8).

Ren, L., Sun, Y., Wang, H., & Zhang, L. (2018). Prediction of bearing remaining useful life with deep convolution neural network. IEEE Access, 6, 13041–13049.

Sharma, S., Abed, W., Sutton, R., & Subudhi, B. (2015). Corrosion fault diagnosis of rolling element bearing under constant and variable load and speed conditions. IFAC-PapersOnLine, 48.

von Hahn, T., & Mechefske, C. K. (2022). Knowledge informed machine learning using a Weibull-based loss function. Journal of Prognostics and Health Management, 2.

Wang, B., Lei, Y., Li, N., & Li, N. (2018). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401–412.

Zhang, W., Li, X., Ma, H., Luo, Z., & Li, X. (2021). Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions. Reliability Engineering & System Safety, 211.
Section
Technical Papers